{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb194719-79de-4b98-b6b4-b8d9ebfe79cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7e3115e-3939-4d65-bb2c-b397c0131a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {\n",
    "    'name': ['David', 'Tim', 'Alice'],\n",
    "    'age': [19, 23, 22]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7994b84c-f715-42b8-a1cd-fc85def37de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas_df = pd.DataFrame(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f4ebbe0-db0a-4e0e-ba1a-fb16ff865c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>David</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tim</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alice</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  age\n",
       "0  David   19\n",
       "1    Tim   23\n",
       "2  Alice   22"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f43d4afa-238d-4d8c-bf93-b78c017ae5b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([95, 87, 94, 90, 43,  8, 61, 16,  3, 99])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(100, size=(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b10b393d-019e-498a-afc4-85a5f1c606ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns = ['age', 'score'])\n",
    "df['score'] = np.random.randint(100, size=(10)) # 0~99的随机整数\n",
    "df['age'] = np.random.randint(25, size=(10)) # 0~24的随机整数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1ba853ae-12bc-4e7a-a9a7-d20de61779b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  score\n",
       "0    7     17\n",
       "1   10      5\n",
       "2   24     73\n",
       "3   14     83\n",
       "4   14     92"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ed55306-9428-4bef-bba0-d6d17a6ecb6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa108afefd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x = 'age', y = 'score', kind = 'scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91df762a-5ce5-4b2f-abf7-0a8aa782e7c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  score\n",
       "0    7     17\n",
       "1   10      5\n",
       "2   24     73\n",
       "3   14     83\n",
       "4   14     92\n",
       "5    8     44\n",
       "6   11     37\n",
       "7   16      0\n",
       "8   16     38\n",
       "9   13     56"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fe6b02bd-af8f-49e0-9afe-d8ae88391830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  score\n",
       "0    7     17\n",
       "5    8     44\n",
       "1   10      5\n",
       "6   11     37\n",
       "9   13     56\n",
       "3   14     83\n",
       "4   14     92\n",
       "7   16      0\n",
       "8   16     38\n",
       "2   24     73"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_df = df.sort_values(by = ['age'])\n",
    "sorted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1c5e310-fa43-4e5e-bc38-7cfc8829bb29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa108f9d810>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_df.plot(x = 'age', y = 'score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "673c28e3-abf7-4823-82d9-2eaba53d9501",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_df.to_csv('pd_df.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f36b7d9b-dcf1-46a4-b915-75bb5a769d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "local_df = pd.read_csv('pd_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ff370f2-63a4-47a3-83cb-ec8bde517f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  score\n",
       "0    7     17\n",
       "1    8     44\n",
       "2   10      5\n",
       "3   11     37\n",
       "4   13     56\n",
       "5   14     83\n",
       "6   14     92\n",
       "7   16      0\n",
       "8   16     38\n",
       "9   24     73"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1cc66b7-4c48-4ad9-b060-d98d0193e677",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = 'https://raw.githubusercontent.com/turingplanet/pandas-intro/main/public-datasets/iris.csv'\n",
    "iris_data_df = pd.read_csv(data_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4da07034-6820-438b-829d-5934a2375c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a93ec891-7958-4aa6-9182-3e7d16c7947c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa10911fad0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data_df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "214f43e7-5a44-40aa-97bc-09f390c1d0e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris_data_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e43c7bb3-7982-4eb9-847d-add68b948b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sepal_length  sepal_width  petal_length  petal_width    species\n",
       "count     150.000000   150.000000    150.000000   150.000000        150\n",
       "unique           NaN          NaN           NaN          NaN          3\n",
       "top              NaN          NaN           NaN          NaN  virginica\n",
       "freq             NaN          NaN           NaN          NaN         50\n",
       "mean        5.843333     3.057333      3.758000     1.199333        NaN\n",
       "std         0.828066     0.435866      1.765298     0.762238        NaN\n",
       "min         4.300000     2.000000      1.000000     0.100000        NaN\n",
       "25%         5.100000     2.800000      1.600000     0.300000        NaN\n",
       "50%         5.800000     3.000000      4.350000     1.300000        NaN\n",
       "75%         6.400000     3.300000      5.100000     1.800000        NaN\n",
       "max         7.900000     4.400000      6.900000     2.500000        NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b338c3c3-cb60-4c47-9e66-792ee2aad970",
   "metadata": {},
   "outputs": [
    {
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       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
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     "execution_count": 21,
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    "iris_data_df"
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  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "12b21852-edd2-4d3e-9822-332e62e8b3f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data_df['sepal_length'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7d332cf2-25dd-406d-9d22-f9d355f6bf34",
   "metadata": {},
   "outputs": [
    {
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       "<p>150 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             NaN          3.5           1.4          0.2     setosa\n",
       "1             NaN          3.0           1.4          0.2     setosa\n",
       "2             NaN          3.2           1.3          0.2     setosa\n",
       "3             NaN          3.1           1.5          0.2     setosa\n",
       "4             NaN          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           NaN          3.0           5.2          2.3  virginica\n",
       "146           NaN          2.5           5.0          1.9  virginica\n",
       "147           NaN          3.0           5.2          2.0  virginica\n",
       "148           NaN          3.4           5.4          2.3  virginica\n",
       "149           NaN          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
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     "execution_count": 23,
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    "iris_data_df"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "228a4a56-57f7-40dd-a163-1aeba0386a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data_df.fillna(100, inplace = True) # 把空值都填为100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a21e0ff-237f-44b8-b91d-cd0b564b2538",
   "metadata": {},
   "outputs": [
    {
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       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0           100.0          3.5           1.4          0.2     setosa\n",
       "1           100.0          3.0           1.4          0.2     setosa\n",
       "2           100.0          3.2           1.3          0.2     setosa\n",
       "3           100.0          3.1           1.5          0.2     setosa\n",
       "4           100.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145         100.0          3.0           5.2          2.3  virginica\n",
       "146         100.0          2.5           5.0          1.9  virginica\n",
       "147         100.0          3.0           5.2          2.0  virginica\n",
       "148         100.0          3.4           5.4          2.3  virginica\n",
       "149         100.0          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3a9ae65b-3c29-4727-8375-e663911c77a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_df['petal_length'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8012313f-0155-4e70-b12e-0cd3750f166c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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